NDLI: Designing a microarray experiment to estimate dominance in maize (Zea mays L.)

Content Provider	Springer Nature Link
Author	Keller, B. Emrich, K. Hoecker, N. Sauer, M. Hochholdinger, F. Piepho, H. P.
Copyright Year	2005
Abstract	Experiments using cDNA microarrays for the identification of genes with certain expression patterns require a thoughtfully planned design. This study was conducted to determine an optimal design for a microarray experiment to estimate differential gene expression between hybrids and their parental inbred lines in maize (i.e. dominance). It has two features: the contrasts of interest contain more than two genotypes and the procedure may be customised to other microarray experiments where different effects may influence hybridisation signals. A mixed model was used to include all important effects. Impacts during growth of the plant material were taken into consideration as well as those occurring during hybridisation. The results of a preliminary experiment were used to determine which effects were to be included in the model, and data from another microarray experiment were used to estimate variance components. In order to select good designs, an optimality criterion adapted to the problem of differential gene expression between hybrids and their parental inbred lines was defined. Two approaches were used to determine an optimal design: the first one simplifies the problem by dividing it into several subproblems, whereas the second is more sophisticated and uses a simulated annealing (SA) algorithm. We found that the first approach constitutes a useful means for designing microarray experiments to study this problem. Using the more sophisticated SA approach the design can be further improved.
Starting Page	57
Ending Page	64
Page Count	8
File Format	PDF
ISSN	00405752
Journal	Theoretical and Applied Genetics
Volume Number	111
Issue Number	1
e-ISSN	14322242
Language	English
Publisher	Springer-Verlag
Publisher Date	2005-05-11
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Content Type	Text
Resource Type	Article
Subject	Genetics Medicine Biotechnology Agronomy and Crop Science

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